BAYESIAN MODELING AND DATA ANALYSIS IN S-PLUS

S-PLUS 中的贝叶斯建模和数据分析

• 批准号: 6489364
• 负责人: JAMES SCHIMERT
• 金额: $ 37.95万
• 依托单位: INSIGHTFUL CORPORATION
• 依托单位国家: 美国
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-04-01 至 2004-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6489364
• 关键词: computer data analysis; computer human interaction; computer program /software; computer system design /evaluation; mathematical model; model design /development; statistics /biometry

The ultimate goal of this project is to provide an extensive suite of Bayesian statistical software tools, utilizing a fast and effective Markov chain Monte Carlo (MCMC) computation engine. The overall implementation will be in the 5-PLUS object-oriented language and system for statistical modeling and data analysis, and the MCMC engine will be implemented in C++, with an efficient interface to 5-PLUS. The implementation will emphasize an ease-of-use paradigm that strongly encourages routine use of Bayesian methods as well as research-oriented exploration for new Statistical techniques by 5-PLUS. Bayesian methods will be developed not only for the most widely used statistical models such as hierarchical models based on exponential family, and linear regression models, but also for more complicated models such as generalized linear mixed models, missing data models and models for robust inference. A large percentage of statisticians in the United States are employed in biostatistics and allied "bio" industries, and a considerable amount of statistical education and research occurs in medical and health related fields. The availability of a broad range of Bayesian statistical methods in a commercially viable data analysis product such as 5-PLUS, will provide an important service to these industries, and to the research and educational needs by supporting and advancing the emerging paradigm of Bayesian modeling and data analysis. PROPOSED COMMERCIAL APPLICATIONS: There is no commercial software offering contemporary Bayesian statistical techniques by MCMC computation. By offering an extensive suite of Bayesian modeling and data analysis toolkit ranging from simple hierarchical models based on exponential families, linear regression models as well as more sophisticated models such as generalized linear mixed models, missing data models and models for robust estimation and interference, we will garner a significant market edge relative to competitors.

该项目的最终目标是利用快速有效的马尔可夫链蒙特卡洛（MCMC）计算引擎提供广泛的贝叶斯统计软件工具。总体实现将以5个以对象为导向的语言和用于统计建模和数据分析的系统，并且MCMC引擎将在C ++中实现，并具有有效的接口为5个以上。 该实施将强调一种易用的范式，强烈鼓励常规使用贝叶斯方法，并以5多个以上的新统计技术为面向研究的探索。贝叶斯方法将不仅针对最广泛使用的统计模型，例如基于指数族的层次模型和线性回归模型，而且还针对更复杂的模型，例如通用线性混合模型，缺少的数据模型和可鲁棒推理的模型。 在美国，很大一部分统计学家受雇于生物统计学和相关的“生物”行业，并且在与医学和健康有关的领域中进行了相当多的统计教育和研究。在商业上可行的数据分析产品（例如5多）中，可用性的贝叶斯统计方法的可用性将通过支持和推进贝叶斯建模和数据分析的新兴范式来为这些行业以及研究和教育需求提供重要服务。拟议的商业应用：没有MCMC Computation提供当代贝叶斯统计技术的商业软件。通过提供广泛的贝叶斯建模和数据分析套件，包括基于指数家族的简单层次模型，线性回归模型以及更复杂的模型，例如通用线性混合模型，缺少的数据模型以及可靠的强大估计和干扰模型，我们将获得与竞争者相对于竞争者的重要市场优势。